Technologies I Work With

Python
C
C++
TypeScript
React
PyTorch
TensorFlow
GenAI
AI Agents
RAG
ONNX
TensorRT
Next.js
Docker
AWS
PostgreSQL
Linux
CUDA
Node.js
Java
FreeRTOS
FPGA

Software Engineer • Embedded Systems • ML/AI

Hi, I'm Abheek Pradhan

Building high-performance applications from edge to cloud to web

Ex-Toshiba SWE InternNSF ResearchHackathon Winner

Abheek Pradhan

Computer Engineering Student | Full stack and embedded systems

Abheek Pradhan

About Me

I am a driven and well-rounded senior at Texas State University. I have a knack for optimization and enjoy working on challenging meaningful problems regardless of the domain. My strongest languages are C, C++, Python and Java.

Outside of programming, I enjoy traveling, gardening, reading, exercising, and various outdoor board sports.

What I'm Working On

  • Research: NSF-funded
    • Submitted - Dual stream Kalman transformer for fall detection and HAR (2-6% improvement across 3 datasets from baselines, tested and validated in real time)
    • AI agents for evaluating real-time performance issues for pervasive computing
    • Video to sensor knowledge distillation
    • Creating pipelines for generating time series data via diffusion
  • Building:
    • Full stack machine learning applications
    • ML inference in C++
    • Agentic analysis systems for monitoring real-time performance in distributed fall detection and HAR applications (server/cloud and offline Android)
  • Exploring: Modern C++, RL (Reinforcement Learning), Unsupervised machine learning
  • Graduating: May 2026 - Texas State University

Work Experience

Software Engineering Intern

Toshiba International Corporation

May 2025 - August 2025

Austin, TX

  • Developed and mocked STM32 FreeRTOS firmware, added and tested features for new touchscreen interface on Toshiba MVDs. Created automated testing infrastructure from scratch on x64, ARM architectures (CMake, TDD, Python, Bash, Ruby), Built Jenkins CI/CD pipeline validating 10,000+ params via unit, integration, and HIL tests to cut QA time by 60%.
  • Optimized RTOS task priorities and DMA scheduling, reducing TouchGFX CPU overhead from 41% to 18%, achieving 25% total system CPU reduction, and eliminating all timing violations to correct issues revealed by my new HIL tests. (C, C++).
  • Engineered a RAG based AI agent using Azure Copilot and OCR for detecting defects in CAD drawings with 94% precision.
CC++STM32FreeRTOSTouchGFXPythonBashRubyCMakeJenkinsAzure

Research Assistant

Texas State University, NSF Funded

August 2024 - Present

San Marcos, TX

  • Developed cross-modal distillation pipeline (video to IMU sensors) and personally trained custom multimodal transformers for time series forecasting under Dr. Anne Ngu. Deployed to edge devices (phones, wearables) with 92% F1 score in real time.
  • Automated dataset cleaning and validation of 15,000+ sensor / video files via LLMs, DSP, and Computer Vision algorithms.
  • Built distributed Ray Slurm pipeline with automated validation and testing on edge devices achieving 1200% speedup, added efficient attention mechanisms increasing F1 +4%, tested DSP + sensor fusion algorithms to align modalities increasing F1 +5%.
  • Refactored multimodal PyTorch / TensorFlow transformer models to edge via TFlite and ONNX using INT8 quantization + mixed-precision training, achieving 2-3x battery life w/ sub-1% accuracy loss allowing for on device inference (Python).
  • Refactored full-stack Android Studio app (Java / Kotlin) to support ONNX, AWS S3, and MongoDB for analytics. Built secure distributed data pipeline with Kafka / Spark for async server-side inference and fault tolerant processing.
PythonPyTorchTensorFlowRaySlurmAndroidJavaKotlinKafkaSparkAWSMongoDB

Machine Learning Engineer

Texas State University

Dec 2024 - Sep 2025

San Marcos, TX

  • Collaborated with research team funded by Texas State C.A.D.S to fine-tune Vision Transformer and MASK R-CNN models on distributed GPU Slurm cluster with Nvidia A100 GPUs. Created custom dataset achieving 98% precision for defect detection.
  • Built production REST API using Python FastAPI, PostgreSQL, and Docker for deployment on Huggingface; implemented server side async request handling and batch processing to handle concurrent requests from React Native mobile app.
  • Accelerated inference via layer fusion and ONNX to TensorRT engine conversion; reducing latency and cloud costs by 40%.
  • Built automated computer vision labeling pipeline leveraging Detectron2, CVAT, and vision LLMs for model-assisted labeling with MLOps pipeline, implemented active learning loop for low-confidence samples, reducing manual labeling hours by 80%.
PythonPyTorchHugging FaceDockerFastAPIPostgreSQLVision TransformersMASK R-CNNTensorRTDetectron2CVAT

Featured Projects

A selection of my recent work showcasing my skills in full-stack development, embedded systems, and AI/ML

Textbook2Video - 2nd Place Antler X Nvidia Hackathon thumbnail

Textbook2Video - 2nd Place Antler X Nvidia Hackathon

TLDR: Live Demo

Agentic LangChain pipeline that turns PDFs into animated, narrated lessons using Deepseek OCR and ElevenLabs TTS; fine-tuned Llama 3 via LoRA + 4b quantization on Brev and containerized the model for live deployment on HuggingFace.

PythonGenAIReactLangchainElevenLabsHuggingface
NeuroNest - ML Defect Detection System thumbnail

NeuroNest - ML Defect Detection System

TLDR: Live Deployment

Production-grade defect detection system using Vision Transformers and MASK R-CNN fine-tuned on distributed A100 GPUs, achieving 98% precision. Built FastAPI REST service with PostgreSQL/Docker deployed on Huggingface; accelerated inference via TensorRT reducing latency and costs by 40%. Implemented automated CV labeling pipeline with Detectron2, CVAT, and vision LLMs, cutting manual labeling hours by 80%.

PythonPyTorchVision TransformersMASK R-CNNFastAPIPostgreSQLDockerTensorRTDetectron2CVATHuggingface
FusionTransformer - Multimodal Fall Detection thumbnail

FusionTransformer - Multimodal Fall Detection

Dual-stream transformer architecture for real-time fall detection on smartwatches, fusing accelerometer and gyroscope data through Kalman filtering. Implements Squeeze-and-Excitation attention and cross-modal knowledge distillation, validated on 51-subject SmartFallMM dataset with LOSO cross-validation.

PythonPyTorchTransformersKalman FiltersSignal ProcessingDeep LearningEdge ML
FPGA-Optimized Facial Recognition YouTube thumbnail

FPGA-Optimized Facial Recognition

TLDR: YouTube

Facial recognition on AMD Kria KV260 SoC achieving 99.47% accuracy with ensemble detection/landmark models; engineered zero-copy DMA + hardware-accelerated GStreamer pipeline with INT8 Vitis AI/Vivado optimizations, delivering 100x CPU speedup and 30–500+ FPS throughput.

CC++Embedded LinuxYoctoPyTorchFPGAVitis AICUDA
Sensor Fusion for Human Activity Recognition thumbnail

Sensor Fusion for Human Activity Recognition

Cross-modal distillation pipeline for fall detection using accelerometer/gyroscope data with Complementary, Madgwick, Mahony, and EKF filters tuned for edge deployment and real-time responsiveness.

PythonSignal ProcessingKalman FiltersEKFNumPyTime Series AnalysisEdge Computing
3D Skeleton Reconstruction from Video thumbnail

3D Skeleton Reconstruction from Video

Implemented GAST-NET for reconstructing 3D human skeletal joints from 2D video, pairing PyTorch-based pose estimation with computer vision preprocessing for motion analysis.

PythonPyTorchComputer VisionDeep Learning3D ReconstructionOpenCV
Time Series Orientation Estimation thumbnail

Time Series Orientation Estimation

Implemented Kalman, Extended Kalman, and complementary filters for IMU orientation estimation and sensor alignment, optimized for low-latency fall detection on embedded devices.

PythonKalman FiltersIMU ProcessingSignal ProcessingNumPySciPyEdge Computing
Smartphone-Based Fall Detection thumbnail

Smartphone-Based Fall Detection

Android fall detection app leveraging on-device IMU streams with TensorFlow Lite/ONNX models, dynamic preprocessing configs, and async sensor pipelines for reliable real-time alerts on constrained hardware.

KotlinJavaAndroidAndroid SDKTensorFlow LiteAI Edge TorchIMU SensorsMachine LearningTime Series Transformers
Autonomous Chess Bot YouTube thumbnail

Autonomous Chess Bot

TLDR: YouTube

Architected multi-process chess system with C++ TCP server, IPC messaging, and browser automation pipeline; containerized deployment and Stockfish orchestration deliver fully autonomous online play with 100% winrate in live games.

C++Stockfish APIMulti-threadingWebSocketsNodeJSDatabase Optimization
Real-Time Audio Transcriber thumbnail

Real-Time Audio Transcriber

Engineered full-stack audio transcription web application with Flask WebSocket backend, React.js/Next.js frontend, hardware-accelerated Whisper AI speech-to-text, and MongoDB session storage.

PythonReactJSNext.jsFlaskWebSocketMongoDBWhisper AI

Education

Bachelor of Science in Computer Science and Engineering

Texas State University

San Marcos, TX

Expected May 2026

Activities and Societies

.EXEACM Member

Skills

AI/ML

  • LLM
  • Transformers
  • CNN
  • Computer Vision
  • Knowledge Distillation
  • MLOps
  • ONNX / TensorRT
  • MCP

Embedded Systems

  • FreeRTOS / RTOS Scheduling
  • STM32 HAL / LL API
  • Kernel Development
  • Device Drivers
  • I2C / SPI / UART
  • JTAG
  • DSP
  • Interrupts
  • Low-noise ADC / Analog Front Ends

Languages

  • Python
  • C
  • C++
  • Java
  • JavaScript
  • TypeScript
  • C#
  • VHDL

Backend & Data

  • Node.js
  • Express.js
  • .NET
  • REST API
  • PostgreSQL
  • MySQL
  • NoSQL
  • Spark

Testing & Process

  • Selenium
  • Agile
  • SCRUM
  • Jira

Networking

  • Network Protocols
  • TCP/IP
  • REST API

Frontend & Frameworks

  • React
  • Next.js
  • Angular
  • Tailwind CSS
  • Qt
  • UI/UX Design
  • Streamlit

Cloud & DevOps

  • AWS
  • Docker
  • Kubernetes
  • CI/CD
  • Git
  • Linux
  • Salesforce